Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective
Haoran Wang, Yongwei Feng, Yonglin Tian, Ziran Wang, Jia Hu, Masayoshi Tomizuka
Abstract
Cooperative Driving Automation (CDA) stands at the forefront of the evolving landscape of vehicle automation, elevating driving capabilities within intricate real-world environments. This research aims to navigate the path toward the future of CDA by offering a thorough examination from the perspective of Planning and Control (PnC). It classifies state-of-the-art literature according to the CDA classes defined by the Society of Automotive Engineers (SAE). The strengths, weaknesses, and requirements of PnC for each CDA class are analyzed. This analysis helps identify areas that need improvement and provides insights into potential research directions. The research further discusses the evolution directions for CDA, providing valuable insights into the potential areas for further enhancement and enrichment of CDA research. The suggested areas include: Control robustness against disturbance; Risk-aware planning in a mixed environment of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs); Vehicle-signal coupled modeling for coordination enhancement; Vehicle grouping to enhance the mobility of platooning.